Crawling Big Data in a New Frontier for Socioeconomic Research: Testing with social tagging

• Main Authors: Juan D. Borrero, Estrella Gualda
• Format: Article
• Language: English
• Published: Research Centre in Tourism, Sustainability and Well-being - CinTurs 2013-03-01
• Series: Journal of Spatial and Organizational Dynamics
• Subjects: information retrieval; social network analysis; collaborative tagging; Web 2.0
• Online Access: http://www.cieo.pt/journal/J_1/article1.pdf

Tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflect the users’ interests and preferences, and also automatically generate folksonomies. Social tagging systems have gained increasing popularity as a method for annotating and categorizing a wide range of different web resources. They also attract researchers in social sciences because they offer a huge quantity of user-generated annotations that reveal the interests of millions of people. To date, the study using digital trace data methods continues to lack a theoretical framework, particularly in social science research. This paper presents a methodology to use big data from Web 2.0 in social research. At the same time, it applies a method to extract data from a particular social bookmarking site (Delicious) and shows the sort of results that this type of analysis can offer to social scientists. The illustration is made around the topic globalization of agriculture Using data crawled from a large social tagging system, can have an important impact in the discovering of latent patterns, which is basic in order to provide effective recommendations to different actors. In this paper a sample of 851 users, 526 URLs and 1,700 tags from the Delicious classification system on the subject of globalization were retrieved and analysed. Through the analysis main users and websites around globalization issues in Delicious emerged, and also the most important tags that were applied by users to describe the globalization of agriculture were discovered. The implications of these methodology and findings for further research are discussed.